• Design, develop, and maintain data pipelines, integrations, and automated data workflows • Build and support data lake, data warehouse, Databricks, and analytics platform solutions • Develop, optimize, and maintain ETL/ELT processes to ingest, transform, and organize data from internal and external systems • Create and maintain analytics-ready datasets, semantic models, and data structures optimized for Power BI consumption, in collaboration with BI Analysts on semantic model design • Develop and maintain data transformation workflows using SQL, Python, Databricks, and other modern data platform tools • Own operational health of the data platform, including monitoring, alerting, incident response, and continuous reliability improvements • Implement data validation, testing, reconciliation, and monitoring processes to ensure data accuracy and integrity • Develop and maintain technical documentation, data dictionaries, lineage documentation, and operational procedures • Collaborate with application developers, BI analysts, and business stakeholders to define data requirements and deliver data solutions • Establish and promote data engineering standards, best practices, and governance processes • Evaluate and recommend improvements to data architecture, tools, processes, and technologies • Administer database and data platform security, including access management, role-based permissions, and auditing of data access • Ensure data solutions comply with privacy, regulatory, and data governance requirements • Manage database and platform operations including backup, recovery, disaster recovery planning, and environment management • Monitor and optimize platform compute utilization and cost, including Databricks cluster configuration and workload efficiency • Implement data retention, archival, and cataloging policies • Participate in solution design, architecture reviews, and strategic data initiatives

• own the long-term architecture and evolution of Cribl's analytics engineering platform • design, build, and maintain certified dbt models as the authoritative source for business-critical metrics • establish and enforce analytics engineering standards for modeling, testing, documentation, and code review • design and maintain semantic and metadata layers that enable reliable AI-powered analytics and self-service • partner with analysts to migrate high-value business logic from Omni into governed warehouse models • partner with Data Engineering to improve source reliability, warehouse architecture, and Snowflake performance and cost efficiency • mentor analysts and analytics engineers on dbt development, data modeling, and analytics engineering best practices • work may happen across many time zones

• Own testing activities from planning through defect resolution and release validation. • Build technical depth across SQL, databases, ETL processes, APIs, and cloud applications. • Strengthen automated testing, data profiling, traceability, and quality practices. • Support healthcare systems where accurate, secure, and dependable data matters. • Review business and technical requirements and translate them into clear test conditions, acceptance criteria, and validation strategies. • Support requirements development through stakeholder discussions, user stories, process analysis, and backlog refinement. • Build, execute, and maintain automated and manual tests using established tools and frameworks. • Develop test plans and test cases for healthcare data, applications, reports, APIs, and user interfaces. • Validate data-ingestion and ETL processes by comparing source data, transformation rules, and target outputs. • Write SQL queries and joins to investigate data quality, confirm business rules, and identify missing, duplicate, inconsistent, or unexpected records. • Perform functional, system, integration, regression, component, and performance testing. • Maintain traceability between requirements, test coverage, defects, and delivery outcomes. • Support user-acceptance testing, defect triage, root-cause analysis, and resolution. • Partner with developers, analysts, product owners, and subject-matter experts to resolve complex application and data issues. • Document test results, risks, defects, and recommendations for technical and non-technical stakeholders. • Help improve testing tools, methodologies, and automation strategies.

• Build and maintain DBT models for cross-domain analytics use cases • Create reusable facts, dimensions, and marts for analytics consumption • Support cross-domain data products such as Churn, LTV, Segmentation, and Customer Intelligence • Implement dbt tests, source freshness checks, and reconciliation logic • Translate business requirements into data models and metric logic • Support data quality checks and model validation • Document model definitions, business rules, assumptions, and lineage • Collaborate closely with analytics and domain teams to ensure datasets are fit for reporting and decision-making